The U.S. Military has several fundamental strategic problems. First, the Army, Navy, Marines and Air Force have very large tactical systems and very small arms systems, on either extreme of the tactical spectrum, but hardly any weapon system in the middle sphere. Second, there is a great need to figure out how to develop automated tactical weapons systems that are powerful, effective, cost-effective and minimize casualties to our military personnel and to friendly noncombatants. Finally, the problem exists of how to organize and coordinate automated weapons to work in a coherent integrated systems structure. The swarm weapon system is intended to address these important challenges.
One of the most extraordinary revolutions in advanced warfare in the last generation consists in the increasing automation of weapons systems. From Vietnam to the Gulf War and from Kosovo to Afghanistan and Iraq, the U.S. military has continued to enhance and rely on automated systems. Such systems include pilotless drones, unmanned surveillance planes and robots as well as remotely launched missiles. The U.S. military is developing pilotless aircraft as well as micro air vehicles for surveillance. Such weapons and unmanned aircraft, which typically require high bandwidth satellite linkage, integrate well with current weapon systems to minimize casualties to our armed forces personnel at reduced cost relative to manned weapon systems and aircraft.
There is, however, a need for sophisticated, networked automated weapon systems that can be adaptive, self-organizing, cost-effective and high performance. Earlier weapons are relatively primitive and stand-alone. What is needed is a network systems approach to automated weapon systems that is both adaptive and interactive in real time.
The next generation of electronic warfare will be unmanned, network oriented and adaptive to the environment. The existence of self-organizing network systems of automated weapons will leverage a more limited group of military personnel and thereby immeasurably increase their warfare productivity. The use of groups of automated weapons in networks of varied weapon systems will provide a substantial force multiplier that will yield a clear sustainable competitive advantage on the battlefield. The use of such advanced technologies will provide “rapid decisive operations” for military forces that use them and defeat for those that do not. The use and implementation of these technologies give clear tactical advantages in the effects-based and collaborative military force of the future. Clearly, then, there is a need for unmanned automated weapon systems.
The U.S. military has developed several categories of unmanned vehicles for land, sea and air. The unmanned air vehicle (UAV), the unmanned ground vehicle (UGV) and the unmanned underwater vehicle (UUV) are used by the Air Force, Army and Navy, respectively, for reconnaissance and attack missions. The UAV is perhaps the most well-known type of automated weapon because of its excellent tactical effectiveness in the battlefield. The two main UAVs used by the U.S. Air Force include the Predator and the Global Hawk. Operated by video satellite feed from a remote human pilot, these drone aircraft have been used successfully in battlefield theatres. The Berkeley UAV project has attempted to construct an automated small helicopter that has added the capability of hovering as well as movement in several directions; such a device would further enhance drone aircraft capabilities. Now in the early stages of development and use, these unmanned vehicles are not generally used in groups that can work together for optimized collective effectiveness.
There are several government and private robotics research projects that use different methods to organize groups of automated vehicles into a coordinated collective. First, the U.S. Air Force has developed a group of four UAVs that can work together as a collective; if one drone is shot down, its program code, including targeting information, is shifted to the other drones so that the mission will continue uninterrupted. Second, Oerlikon Contraves, a Swiss company, has developed a system (U.S. Pat. No. 6,467,388 B1, Oct. 22, 2002) to coordinate the behavior of several automated (space-based) fire control units; such a system is useful in an antiballistic missile context. Third, iRobot, a Cambridge, Mass., company, has developed a system of networked line-of-sight wireless automated robots for industrial applications. Fourth, Sandia Lab has developed a system of automated robots for use by the U.S. Army. This system utilizes UGVs with video feeds that link into a larger system for coordinated missions. Fifth, the U.S. Navy has experimented with UUVs for mine or submarine detection and attack. Combinations of the Remus small submarine work together to form a “Sculpin” team for a common, if not fully coordinated, antimine mission. The Navy also has developed a larger Battlespace Preparation Automated Underwater Vehicle (BPAUV) for detecting and attacking enemy submarines in hostile waters. Finally, NASA has developed exploration systems comprised of multiple robotic vehicles that network together for a common exploratory interplanetary mission utilizing AI and complex expert systems. Each of these systems provides an attempt at self-organized collectives of robotic systems by using limited technologies.
On the academic research side, there are several projects involving the coordination of groups of automated robots. Theoretical research performed at the Santa Fe Institute, a think tank focused on complexity theory for mathematical, biological, computational and economic applications, has been a leader in intelligent systems. Their interdisciplinary research has sought to develop models for collective robotics. A Santa Fe researcher, Bonabeau, developed research into complex behavior-based artificial systems by using a combination of rules that emulate self-organizing natural systems such as ant, bee or wasp organizational collectives. These complex natural systems, developed from millions of years of evolution, represent a key model for artificial intelligence scholars to develop automated systems.
Researchers at MIT and at Georgia Tech have also been active in the field of collective robotics. By using concepts from artificial intelligence that are applied to individual robotics, researchers have begun to build complex models for groups of robots. Some researchers have developed architectures for collective robotic systems that involve a combination of central control and behavior-based control. There are advantages and disadvantages of each main model. However, by developing unique hybrid control architectures, researchers seek to overcome the limits of each model.
Central control has some key advantages for robotics research. By using a central planner, the system can use logic to solve problems from the top down. Such a model produces deliberate and predictable results. A central control model can use hierarchy to organize a robotic system, which provides a clear command structure. Because it is predictable, a centralized control system can also use simulations to test various possible outcomes. Such a system is useful in order to achieve general strategic objectives without interference. Having a centralized control also provides a clear source for moral responsibility if a mission fails because the programmer is responsible for the results of a mission. The main problem, however, is that central systems cannot plan well in an uncertain or unpredictable environment in which there is change.
Behavior-based models of robotic systems, on the other hand, combine combinations of behaviors to achieve a specific outcome. By combining functions such as path creation and following, navigation, obstacle detection and avoidance and formation control, robots can construct reconnaissance activities. Such systems are ideal for interacting with complex environments in real time because they immediately react to specific inputs. In addition to their faster responses, such systems require less computation and communication resources than central control models. This approach to robotic control, however, lacks the planning needed for optimal coordination between groups of robots for a common objective.
There are several main hybrid models of robotic control systems in the academic world that are noteworthy. First, the AuRa system uses “selection” models in which the planning component determines the behavioral component. Second, the Atlantis model, developed by NASA, uses “advice” planning in which advice is provided but the reactor level actually decides. Third, the “adaptation” model continuously alters reaction by focusing on changing conditions. Finally, the “least commitment” model uses a postponement strategy in which the planner defers a decision until the last possible moment. These hybrid control models are used for individual robot actions. However, versions of these systems can be used for organizing groups of robots as well.
There are several systems that have sought to develop distinctive models for group robotic action by using unique combinations of hybrid control architectures. The Nerd Herd applies several behaviors in combination, specifically, homing, aggregation, dispersion, following and safe wandering, to achieve organized action. The Alliance model adds motivational behaviors to the subsumption approach with heterogeneous robot teams. The L-Alliance model evolves learning behaviors based on a statistical evaluation of the histories of other robots' performances. The Society Agency model develops team cooperation without any explicit inter-robot communications.
These systems use combinations of behaviors with a central control module to create social behaviors. For instance, the combination of behaviors for sensing and foraging can be added together in order to solve surveillance problems. If a number of coordinated robots can work together in organized patterns, surveillance problems can be solved faster and more completely using complex group behaviors. In another example, groups of robots can be organized into four two-dimensional formations (wedge, diamond, line and column) to perform tasks by using a hybrid control model that uses behaviors to adapt to the environment. Additional three-dimensional formations (geodesic sphere and geodesic arc) and four-dimensional formations (complex sequences and transformation of configurations) can be optimized for environmental interaction. Finally, the robot teams may include a heterogeneous colony of multifunctional robots that, in combination, may self-organize in order to perform more complex tasks than a number of specialist drones could accomplish.
Developing methods to organize collectives of automated robotic vehicles is one of the most challenging and complex problems in computer science, artificial intelligence and robotics research. These challenges involve the need to develop original technological approaches in computation, communications, networking, materials, energy supply and artificial intelligence.
The present invention develops a novel hybrid architecture for use with automated groups of mobile robotic vehicles in a multirobotic system. The swarm system has numerous applications.